Summary

This project seeks to improve on the Howard et al. (2020) methods used to estimate sport fish harvest, catches and releases of rockfish in Alaska waters. This is essentially a Bayesian version of the Howard methods that allows for more appropriate and defensible sharing of information between areas, handles missing data in a more appropriate manor, accurately propagates uncertainty throughout the estimation procedure and thus does not rely on the decision tree approach in the original Howard methods. Furthermore, the Bayesian approach should provide sport fish harvest, catch and mortality estimates back to 1978 when the SWHS was implemented. Harvest estimates should be mostly consistent with Howard estimates during contemporary times, but may differ based on more appropriate weighting of SWHS and logbook data, including estimating and correcting bias in the SWHS data. Furthermore, the Howard methods are wholly reliant on logbook release estimates and ignore the release estimates from the SWHS data (inferred from the catch and harvest estimates). Here we explore several models that attempt to balance all of the data in estimating releases.

Data

Harvest data was available for 22 commercial fishing management areas in Southcentral and Southeast Alaska. Areas with negligible rockfish harvest were pooled with adjacent areas for analysis. Specifically the Aleutian and Bering areas were pooled into an area labeled BSAI; the IBS and EKYT were pooled into an area labeled EKYKT; the Southeast, Southwest, SAKPEN and Chignik areas were pooled into an area labeled SOKO2PEN and the Westside and Mainland areas were pooled into an area labeled WKMA.

Stateside Harvest Survey (SWHS)

Statewide harvest survey estimates of rockfish catch and harvest are available for 28 years (1996-2023) for all users and for 13 years (2011-2023) for guided anglers (Figure 0). Additionally, there are estimates from 1977- 1995 that required some partitioning work to ascribe to current management units. Harvests in unknown areas were apportioned based on harvest proportions in 1996. Variance estimates are not available for pre-1996 data and as such, the maximum observed coefficient of variation (cv) in each commercial fisheries management unit was applied.

**Figure 0.**- Data sources for estimating rockfish harvests and releases in ADF&G commercial fisheries management units.

Figure 0.- Data sources for estimating rockfish harvests and releases in ADF&G commercial fisheries management units.


SWHS estimates are believed to be biased to some degree. These modelling efforts aim to estimate and correct for that bias with the assumption that logbook harvests are a census of guided harvests.

Rockfish release estimates are inferred from the difference between catch and harvest estimates.

Adam noted that the first 5 years (23 years counting the historical data) in the SWHS data set for PWSO seem unreasonable (close to zero and not corroborated with logbook estimates). Adam recommended setting these harvests to unknown, but current model development has included the data. Once a satisfactory model has been identified we will exam the effects of censoring the PWSO data.

Creel Surveys

NA

Guide Logbooks

Sport fishing guides were required to report their harvest of rockfish for 26 years (1998-2023). Reported harvest is also available by assemblage (pelagic vs. non-pelagic). Harvest of yelloweye rockfish were reported separately beginning in 2006.

Logbooks also record the number of rockfish released for the same categories. However, the reliability of the release data is somewhat questionable as reported releases are generally far lower than that estimated by the SWHS. As such several treatments of the data are considered.

Logbook versus SWHS estimates

Estimates of guided harvests and releases from the SWHS do not align with the census from charter logbooks. Logbook harvest reports are generally considered reliable and are used to assess the bias in SWHS reports. However, there is even greater disparity between release estimates in the two sources and it is debatable whether logbook releases should be treated as a census. The Howard et al. (2020) methods do treat the logbook release data as “true” and thus are considerably less than would be estimated from the SWHS data.

**Figure 1.**- SWHS harvest estimates from guided trips (Hhat) versus repoted harvests from charter logbooks (H_lb).

Figure 1.- SWHS harvest estimates from guided trips (Hhat) versus repoted harvests from charter logbooks (H_lb).


The Howard methods treat the logbook release data as a census and then use the ratio of guided:unguided releases in the SWHS to expand the logbook release estimates to generate total and unguided estimates.

To evaluate this discrepancy, several models were used to estimate releases in this exploration. One method (\(LB_{fit}\)) considers the logbook release data to be reliable and a second method (\(LB_{cens}\)) treats the logbook release data as estimates of the minimum released, thus giving more weight to SWHS release estimates. A third method (\(LB_{hyb}\)) is a hybrid approach that treats reported releases of yelloweye as reliable but total rockfish and pelagic rockfish releases as minimums. Model development to date has revealed a tension between the total and pelagic logbook releases and the yelloweye logbook releases.

Composition data

Harvest sampling data exists from Gulf of Alaska areas since 1996 and from Southeast Alaska areas since 2006. Port sampling data is comprised of the number of total rockfish, pelagic and non-pelagic rockfish, black rockfish and yelloweye rockfish.

A current challenge at this juncture is how to accommodate the prohibition on retaining yelloweye in Southeast from 2020 through 2024. Because it is closed to retention the port sampling data is not reflective of releases while remaining an accurate description of the harvest. Current modelling efforts revolve around developing a separate yelloweye curve that censors the missing data.

Process equations

The true harvest \(H_{ay}\) of rockfish for area \(a\) during year \(y\) is assumed to follow a temporal trend defined by a penalized spline:

\[\begin{equation} \textrm{log}(H_{ay})~\sim~\textrm{Normal}(f(a,y), {\sigma_H}) \end{equation}\]

where \(f(a,y)\) in a p-spline basis with 7 components (knots) and a second degree penalty. The variance, \(\sigma_H\), was given a normal prior with a mean and standard deviation of 0.25 and 1, respectively.

Charter and private harvest \(H_{ayu}\) (where u = 1 for charter anglers and u = 2 for private anglers) is a fraction of total annual harvest in each area:

\[\begin{equation} H_{ay1}~=~H_{ay}P_{(user)ay1}\\H_{ay2}~=~H_{ay}(1-P_{(user)ay1}) \end{equation}\]

where \(P_{(user)ay1}\) is the fraction of the annual harvest in each area taken by charter anglers. \(P_{(user)ay1}\) was modeled hierarchically across years as:

\[\begin{equation} P_{(user)ay1}~\sim~\textrm{beta}(\lambda1_a, \lambda2_a) \end{equation}\]

with non-informative priors on both parameters.

Annual black rockfish harvest \(H_{(black)ayu}\) for each area and user group is:

\[\begin{equation} H_{(black)ayu}~=~H_{ayu}P_{(pelagic)ayu}P_{(black|pelagic)ayu} \end{equation}\]

where \(P_{(pelagic)ayu}\) is the fraction of the annual harvest for each area and user group that was pelagic rockfish and \(P_{(black|pelagic)ayu}\) is the fraction of the annual harvest of pelagic rockfish for each area and user group that was black rockfish. Annual yelloweye rockfish harvest \(H_{(yelloweye)ayu}\) for each area and user group:

\[\begin{equation} H_{(yelloweye)ayu}~=~H_{ayu}(1-P_{(pelagic)ayu})P_{(yelloweye|non-pelagic)ayu} \end{equation}\]

where \(P_{(yellow|non-pelagic)ayu}\) is the fraction of the annual harvest of non-pelagic rockfish for each area and user group that was yelloweye rockfish. The composition parameters (\(P_{(pelagic)ayu}\), \(P_{(black|pelagic)ayu}\), \(P_{(yelloweye|non-pelagic)ayu}\)) were modeled using a logistic curve that would allow hindcasting without extrapolating beyond the limit of observed values such that:

\[\begin{equation} \textrm{logit}(P_{(comp)ayu})~=~\beta1_{(comp)ayu} + \frac{\beta2_{(comp)ayu}}{(1 + exp(\beta3_{(comp)ayu}*(y - \beta4_{(comp)ayu})))} + \beta5_{(comp)ayu}*I(u=private)+re_{(comp)ayu} \end{equation}\]

where the \(\beta\) parameters define the intercept, scaling factor, slope, inflection point and private angler effect, respectively, \(y\) is the year index, \(I(u=private)\) is an index variable which is 1 when the user groups is private and 0 otherwise and \(re_{(comp)ayu}\) is a random effect with a non-informative prior.

The true number of released rockfish \(R_{ayu}\) were based on the proportion of the total catch harvested by area, year, user group and species grouping (pelagic or yelloweye), \(pH_{(comp)ayu}\). Thus, converting \(H_{(comp)ayu}\) to total catches by user group, \(C_{(comp)ayu}\), with \(pH_{(comp)ayu}\) results in estimates of total releases such that

\[\begin{equation} R_{(comp)ayu}~=~ C_{(comp)ayu} - H_{(comp)ayu} ~=~ \frac{H_{(comp)ayu}}{pH_{(comp)ayu}} - H_{(comp)ayu} \end{equation}\]

with total releases calculated from the sum of the user group releases. The proportion of total rockfish harvested by user group, \(pH_{ayu}\), was assumed to be the mean of \(pH_{(pelagic)ayu}\), \(pH_{(yelloweye)ayu}\) and \(pH_{(nonpel-nonYE)ayu}\) weighted by the relative harvest \(H_{(comp)ayu}\) such that

\[\begin{equation} R_{ayu}~=~ \frac{\sum ({H_{(comp)ayu} * pH_{(comp)ayu})}}{\sum {H_{(comp)ayu}}} \end{equation}\]

The proportion harvest parameters \(pH_{(pelagic)ayu}\) and \(pH_{(yelloweye)ayu}\) were modeled using a logistic curve that would allow hindcasting based on trends in the data without extrapolating beyond the range of observed values such that

\[\begin{equation} \textrm{logit}(pH_{ayuc})~=~\beta1_{(pH)ayuc} + \frac{\beta2_{(pH)ayuc}}{(1 + exp(\beta3_{(pH)ayuc}*(y - \beta4_{(pH)ayuc}))) + \beta34_{(pH)ayuc}} \end{equation}\]

A random effect term allowed estimation during the historical period when data is available, but the curve defined by the above equation determined release estimates between 1977 and 1990.

\(pH_{(nonpel-nonYE)ayu}\) was modeled separately with an informative prior centered around a \(pH\) of 0.8 such that

\[\begin{equation} pH_{(nonpel-nonYE)ayu}~\sim~\textrm{beta}(in development) \end{equation}\]

Observation equations

SWHS estimates of annual rockfish harvest \(\widehat{SWHS}_H{ay}\) were assumed to index true harvest:

\[\begin{equation} \widehat{SWHS}_H{ay}~\sim~\textrm{LogNormal}\left(\textrm{log}(H_{ay}b_{ay}), \sigma_{SWHSHay}^2\right) \end{equation}\]

where bias in the SWHS harvest estimates \(b_H{ay}\) is modeled hierarchically across years as:

\[\begin{equation} b_H{ay}~\sim~\textrm{Normal}(\mu_H{(b)a}, \sigma_H{(b)a}) \end{equation}\]

with non-informative priors on both parameters.

SWHS estimates of guided angler harvest \(\widehat{SWHS}_H{ay1}\) are related to total harvest by:

\[\begin{equation} \widehat{SWHS}_H{ay1}~\sim~\textrm{LogNormal}\left(\textrm{log}(H_{ay1}b_{ay}), \sigma_{SWHS_{ay1}}^2\right) \end{equation}\]

Reported guide logbook harvest \(\widehat{LB}_H{ay}\) is related to true harvest as:

\[\begin{equation} \widehat{LB}_H{ay}~\sim~\textrm{Poisson}(H_{ay1})\\ \widehat{LB}_H{(pelagic)ay}~\sim~\textrm{Poisson}(H_{ay1}P_{(pelagic)ay1})\\ \widehat{LB}_H{(yelloweye)ay}~\sim~\textrm{Poisson}(H_{(yelloweye)ay1})\\ \end{equation}\]

SWHS estimates of annual rockfish releases \(\widehat{SWHS}_R{ay}\) were assumed to index true releases in a similar fashion and thus modeled similarly. Because logbook release data is more questionable and demonstrates greater disagreement with SWHS estimates (Figure 1), several approaches have been explored. In the first approach model \(LB_{fit}\) treats the release data as a true census and the releases are related to true releases just as harvests were modelled such that:

\[\begin{equation} \widehat{LB}_R{ay}~\sim~\textrm{Poisson}(R_{ay1})\\ \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{Poisson}(R_{ay1}P_{(pelagic)ay1})\\ \widehat{LB}_R{(yelloweye)ay}~\sim~\textrm{Poisson}(R_{(yelloweye)ay1})\\ \end{equation}\]

In the second approach we consider the logbook release data to be a minimal estimate of the true releases. Thus model \(LB_{cens}\) censors the release data (censored data is entered as NA) and treats the reported releases as a minimal number such that

\[\begin{equation} \text{censored} \widehat{LB}_R{ay}~\sim~\textrm{LogNormal}\left(\log(R_{ay}), 1\right)\text{T}\left(\widehat{LB}_R{ay}, \infty\right)\\ \text{censored} \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(pelagic)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(pelagic)ay}, \infty\right)\\ \text{censored} \widehat{LB}_R{(ye)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(ye)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(ye)ay}, \infty\right)\\ \end{equation}\]

Model \(LB_{hyb}\) is a hybrid approach that treats the yelloweye releases as a reliable census of yelloweye releases (given the emphasis and ease of recording these fish) but censors the pelagic and total rockfish release estimates such that

\[\begin{equation} \text{censored} \widehat{LB}_R{ay}~\sim~\textrm{LogNormal}\left(\log(R_{ay}), 1\right)\text{T}\left(\widehat{LB}_R{ay}, \infty\right) \\ \text{censored} \widehat{LB}_R{(pelagic)ay}~\sim~\textrm{LogNormal}\left(\log(R_{(pelagic)ay}), 1\right)\text{T}\left(\widehat{LB}_R{(pelagic)ay}, \infty\right) \\ \widehat{LB}_R{(yelloweye)ay}~\sim~\textrm{Poisson}(R_{(yelloweye)ay1})\\ \end{equation}\]

SWHS estimates of guided angler release \(\widehat{SWHS}_R{ay1}\) is modeled the same as harvests.

SWHS release bias was modeled differently in the \(LB_{fit}\), \(LB_{cens}\), and \(LB_{hyb}\) models. Because the \(LB_{fit}\) model assumes that logbook release data is true and the poison likelihoods assume a much smaller variance than the large variances associated with the SWHS release estimates, SWHS release estimates \(b_R{ay}\) were modelled independently of the harvest bias \(b_H{ay}\) such that

\[\begin{equation} b_R{ay}~\sim~\textrm{Normal}(\mu_R{(b)a}, \sigma_R{(b)a}) \end{equation}\]

where bias in the SWHS release estimates \(b_R{ay}\) is modeled hierarchically across years as:

\[\begin{equation} b_R{ay}~\sim~\textrm{Normal}(\mu_R{(b)a}, \sigma_R{(b)a}) \end{equation}\]

with non-informative priors on both parameters.

The \(LB_{cens}\) model treats the logbook release data as lower bound on the release estimates and thus the likelihood linking true releases to the SWHS release estimates is dominant. During model development it was apparent that estimating bias in the SWHS data was more difficult and a different structure was employed that assumed bias in SWHS release data followed a similar pattern to that of the harvests but is offset by some area specific amount. In these models \(b_R{ay}\) differed from \(b_H{ay}\) by offset \(Rboff_{a}\) such that

\[\begin{equation} b_R{ay}~=~b_H{ay} + Rboff_{ay} \end{equation}\]

where

\[\begin{equation} Rboff_{a}~\sim~\textrm{Normal}(\mu_{(bR)r}, \sigma_{(bR)r}) \end{equation}\]

such that \(Rboff_{a}\) was modeled hierarchically across region r.

The number of pelagic rockfish sampled in harvest sampling programs \(x_{(pelagic)ayu}\) follow a binomial distribution:

\[\begin{equation} x_{(pelagic)ayu}~\sim~\textrm{Binomial}(P_{(pelagic)ayu}, N_{ayu}) \end{equation}\]

where \(N_{ayu}\) is the total number of rockfish sampled in area \(a\) during year \(y\) form user group \(u\). The number of black rockfish sampled in harvest sampling programs and the number of yellow rockfish sampled modeled analogously with an appropriately substituted \(N\).

Unresolved issues and outstanding questions:

Models detailed in this markdown represent the next step in the modelling process whereby the pH parameters are separated out by species. This approach separates the compositional data that is germaine to the harvests from the release estimates and releases are now based on pH. Additionally, this approach allows the pH parameters to differ between pelagic and yelloweye which is appropriate given regulatory changes as well as fisherman and industry behaviour and is born out in the results. The approach results in great uncertainty around unguided release estimates, but that uncertainty is appropriate given the data. These models handle the yelloweye closures in southeast much more appropriately given that the compositional data is no longer directly applied to the release estimates. These versions of the model are in development and it is unclear whether the \(LB_{cens}\) model would work, but it appears applicable to the \(LB_{fit}\) and \(LB_{hyb}\) approaches.

Other issues include:

  1. Complete convergence has not been achieved and the logistic curve parameters for p_pelagic and p_yellow remain the last sticking point. I think that p_pelagic will resolve with longer chains.
  2. Estimate precision: These models are producing more precise harvest estimates that in Adam’s original model. I am not sure why at this juncture. sigma_H on the spline was switched from a fixed value to a prior centered around that fixed value, but the model estimates are in the same range as the fixed value. Would the number of knots in the spline explain this? 7 knots was settled on during early model fitting when it clearly performed better than fewer or more knots.
  3. Prior choices in general need to be vetted. The priors on the logistic curves are fairly informed in an effort to achieve the desired shapes for hindcasting. Ideally, sensitivity testing would occur but the model is very slow to converge. The beta parameters on the logistic curves have required a lot of work on the priors to reach convergence.
  4. Random effects on pH: These are currently used in the model but because pH isn’t linked to data as the p_comp data is I am not sure what to make of them or if they are appropriate.
  5. Model comparisons: I need to write code for comparing models side by side as well as quantifying the differences between these methods and the Howard methods.

Results

**Figure X.**- Rhat values and proportion of parameters that converged (Rhat < 1.1.)

Figure X.- Rhat values and proportion of parameters that converged (Rhat < 1.1.)

Estimate comparison

Since previous estimates of rockfish harvest have been produced these first 3 graphs will be used to show how the modeled estimates compare to the estimates produced earlier. For total rockfish the estimates are in general agreement although differences are noted. These estimates should be more reliable because they include both SWHS and guide logbook data, handle variance more appropriately, use hierarchical distributions when data is missing, directly consider observation error and are produced using reproducible research.

**Figure 2.**- Total rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 2.- Total rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 3.**- Total rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 3.- Total rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.


Notes from Adam: When looking at only black rockfish the most significant differences are for the Prince William Sound Inside area. I did not spend a great deal of time tracking this down although it looks like the previous version used bad values for \(P_{(black)ayu}\) for at least unguided anglers. For the moment I would ignore the results for BSIA and SOKO2SAP. I think it is possible to give approximate values for these areas but it will require a little more coding which I have yet to do.

**Figure 4.**- Black rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 4.- Black rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.


And black rockfish releases…

**Figure 5.**- Black rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 5.- Black rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 6.**- Yellow rockfish harvests 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 6.- Yellow rockfish harvests 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.



**Figure 7.**- Yellow rockfish releases 1996--2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Figure 7.- Yellow rockfish releases 1996–2023. Lines and error polygons represent model estimates and points and error bars represent Howard et al estimates.

Model fit

Logbook residuals

**Figure 8.**- Residuals from logbook harvests

Figure 8.- Residuals from logbook harvests


SWHS residuals

**Figure 9.**- Residuals from SWHS harvests.

Figure 9.- Residuals from SWHS harvests.



**Figure 10.**- Residual of SWHS releases

Figure 10.- Residual of SWHS releases

Parameter estimates

P(Charter)

These histograms show the posterior distribution of the mean percent of rockfish harvested by the charter fleet.

**Figure 11.**- Mean percent of harvest by charter anglers.

Figure 11.- Mean percent of harvest by charter anglers.


When considered annually we see the percent of rockfish harvested by the charter fleet follows our data fairly well although we just do not have much information about this ratio. Prior to 2011 the percent charter is confounded with SWHS bias and should be mostly discounted.

**Figure 12.**- Annual estimates of the percent of harvest by charter anglers for 16 commerical fishing manamgent areas, 1996-2023.

Figure 12.- Annual estimates of the percent of harvest by charter anglers for 16 commerical fishing manamgent areas, 1996-2023.

P(Harvest)

These plots show the fitted logistic line to the proportion of caught rockfish that are harvested. These estimates are used for hindcasting catch estimates based on the harvest data in early years when catch estimates are unavailable.

**Figure 13.**- Annual proportion of all rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of all rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of pelagic rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of pelagic rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.


**Figure 13.**- Annual proportion of non-pelagic, non-yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

Figure 13.- Annual proportion of non-pelagic, non-yelloweye rockfish catch that was harvested. Note that pre-1990 estimates are used to estimate catch in these years when catch estimates are not available.

SWHS bias

Figure 14 shows the mean estimate for SWHS bias. Cook Inlet, North Gulf Coast and North Southeast Inside all look pretty good while most other areas have substantial bias. Prince William Sound Inside has the largest bias.

**Figure 14.**- Mean SWHS bias for harvests and catches. Note that a bias < 1 indicates that the SWHS *underestimates* the true value and bias > 1 indicates the survey *overestimates* the true value.

Figure 14.- Mean SWHS bias for harvests and catches. Note that a bias < 1 indicates that the SWHS underestimates the true value and bias > 1 indicates the survey overestimates the true value.


Our estimates of SWHS bias track observations fairly well when he have guided harvest estimates. There are some disturbing trends/patterns seen in the earlier time periods. Often the patterns represent periods where SWHS estimates and guide logbook estimates do not follow the recent relationship. I’m not sure what drives the trends but it seems plausible to me that long-term changes in the ratio of charter and private anglers may be a factor. If Charter/Private ratio information is available in the historical creel data it my be helpful here (particularly for North Southeast Inside and South Southeast outside).

**Figure 15.**- Annual estimates of SWHS bias in harvests and releases for 16 commerical fishing manamgent areas, 1996-2023. Note that a bias < 1 indicates that the SWHS *underestimates* the true value and bias > 1 indicates the survey *overestimates* the true value.

Figure 15.- Annual estimates of SWHS bias in harvests and releases for 16 commerical fishing manamgent areas, 1996-2023. Note that a bias < 1 indicates that the SWHS underestimates the true value and bias > 1 indicates the survey overestimates the true value.

P(pelagic)

We model the percentage of pelagic rockfish in the harvest because we have the information for charter anglers (via logbooks) starting in 1998. Other than looking at the model estimates you can use Figure 8 to compare the two data streams for pelagic rockfish harvest. In general they are in agreement with major exceptions in Price William Sound inside, Prince William Sound outside (early in the time series) and South Southeast inside.

**Figure 16.**- Annual estimates of the percent of the sport harvest that was pelagic rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 16.- Annual estimates of the percent of the sport harvest that was pelagic rockfish for 16 commerical fishing manamgent areas, 1996-2023.

P(black|pelagic)

Note that in Southeast Alaska we only have composition data starting in 2006. Tania dug up old SE data, but it did not provide any useful data for species apportionment.

**Figure 17.**- Annual estimates of the percent of the sport harvest of pelagic rockfish that were black rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 17.- Annual estimates of the percent of the sport harvest of pelagic rockfish that were black rockfish for 16 commerical fishing manamgent areas, 1996-2023.

P(yelloweye|non-pelagic)

**Figure 18.**- Annual estimates of the percent of the sport harvest of non-pelagic rockfish that were yelloweye rockfish for 16 commerical fishing manamgent areas, 1996-2023.

Figure 18.- Annual estimates of the percent of the sport harvest of non-pelagic rockfish that were yelloweye rockfish for 16 commerical fishing manamgent areas, 1996-2023.



Summary of unconverged parameters:

Table 1. Summary of unconverged parameters including the number (n) and the average Rhat from the unconverged parameters.
parameter n badRhat_avg
beta3_pH 6 3.581591
beta2_pH 3 3.070877
beta1_pH 5 2.322278
tau_beta0_pH 4 1.927333
beta3_pelagic 1 1.554600
beta2_yellow 4 1.502953
beta1_pelagic 5 1.464904
beta0_pelagic 5 1.422804
beta0_yellow 3 1.287232
parameter n badRhat_avg
beta3_yellow 1 1.280034
tau_beta0_pelagic 2 1.222436
beta0_pH 11 1.187863
beta1_yellow 3 1.163669
beta2_pelagic 4 1.159046
tau_beta0_yellow 1 1.149085
mu_beta0_pelagic 1 1.131245
mu_beta0_pH 1 1.126149
Table 2. Summary of unconverged parameters by area
CI CSEO EWYKT NG northeast NSEI NSEO PWSI PWSO SSEI SSEO WKMA
beta0_pelagic 1 1 0 1 1 0 0 0 1 0 0 0
beta0_pH 1 1 1 0 0 1 1 1 0 1 1 0
beta0_yellow 0 0 1 0 0 0 0 1 0 0 0 1
beta1_pelagic 1 1 0 1 1 0 0 1 0 0 0 0
beta1_pH 0 0 0 0 0 0 1 0 0 1 0 1
beta1_yellow 0 0 0 1 0 0 0 1 0 0 0 1
beta2_pelagic 0 1 1 0 1 1 0 0 0 0 0 0
beta2_pH 0 0 1 0 0 0 1 0 0 1 0 0
beta2_yellow 0 0 1 1 0 0 0 0 1 0 0 1
beta3_pelagic 0 1 0 0 0 0 0 0 0 0 0 0
beta3_pH 1 0 0 0 0 0 1 1 0 1 0 0
beta3_yellow 0 0 0 0 0 0 1 0 0 0 0 0
mu_beta0_pelagic 0 0 0 0 0 0 0 1 0 0 0 0
mu_beta0_pH 1 0 0 0 0 0 0 0 0 0 0 0
tau_beta0_pelagic 1 0 0 1 0 0 0 0 0 0 0 0
tau_beta0_pH 1 0 0 0 0 0 0 1 0 0 0 0
tau_beta0_yellow 1 0 0 0 0 0 0 0 0 0 0 0

Parameter estimates:

Summary Table of Parameter Estimates
Parameter mean sd Lower_CI Median Upper_CI
mu_bc_H[1] -0.155 0.069 -0.286 -0.157 -0.016
mu_bc_H[2] -0.121 0.037 -0.185 -0.124 -0.039
mu_bc_H[3] -0.462 0.072 -0.595 -0.463 -0.316
mu_bc_H[4] -1.134 0.214 -1.575 -1.131 -0.734
mu_bc_H[5] 0.554 0.700 -0.347 0.451 1.939
mu_bc_H[6] -2.208 0.317 -2.820 -2.215 -1.582
mu_bc_H[7] -0.486 0.114 -0.721 -0.483 -0.265
mu_bc_H[8] 0.151 0.354 -0.467 0.127 0.948
mu_bc_H[9] -0.322 0.134 -0.587 -0.323 -0.055
mu_bc_H[10] -0.130 0.063 -0.253 -0.130 -0.005
mu_bc_H[11] -0.129 0.036 -0.201 -0.129 -0.059
mu_bc_H[12] -0.266 0.107 -0.495 -0.263 -0.069
mu_bc_H[13] -0.145 0.079 -0.296 -0.146 0.012
mu_bc_H[14] -0.323 0.101 -0.533 -0.322 -0.129
mu_bc_H[15] -0.353 0.047 -0.442 -0.353 -0.259
mu_bc_H[16] -0.302 0.367 -0.964 -0.324 0.458
mu_bc_R[1] 1.758 0.900 -0.393 1.952 3.069
mu_bc_R[2] 1.149 0.831 -0.626 1.270 2.443
mu_bc_R[3] 1.431 0.723 -0.215 1.593 2.448
mu_bc_R[4] 0.010 1.169 -2.254 0.070 2.170
mu_bc_R[5] 1.776 1.103 -0.118 1.659 4.357
mu_bc_R[6] 0.782 0.609 -0.541 0.838 1.851
mu_bc_R[7] 1.777 1.137 -1.157 2.161 3.109
mu_bc_R[8] 2.015 0.811 -0.138 2.201 3.099
mu_bc_R[9] 2.441 1.172 -0.553 2.675 4.107
mu_bc_R[10] 2.269 1.418 -1.057 2.461 4.482
mu_bc_R[11] 1.204 0.274 0.640 1.211 1.723
mu_bc_R[12] -0.506 0.567 -1.576 -0.535 0.717
mu_bc_R[13] 0.477 0.373 -0.314 0.489 1.163
mu_bc_R[14] 0.480 0.398 -0.331 0.487 1.246
mu_bc_R[15] 0.329 0.255 -0.175 0.321 0.862
mu_bc_R[16] 1.190 0.418 0.043 1.240 1.858
tau_pH[1] 28520.603 267153.041 65.164 686.515 119922.286
tau_pH[2] 1.225 0.353 0.665 1.340 1.773
tau_pH[3] 2.854 0.716 1.666 2.999 4.058
beta0_pH[1,1] 1.764 1.171 -0.890 1.920 3.640
beta0_pH[2,1] 1.998 1.031 -0.096 2.067 3.765
beta0_pH[3,1] 1.982 1.174 -0.632 2.080 4.017
beta0_pH[4,1] 1.705 1.725 -1.987 1.820 5.007
beta0_pH[5,1] 2.672 2.003 -0.611 2.451 7.275
beta0_pH[6,1] 3.521 2.736 -0.072 2.980 9.908
beta0_pH[7,1] 3.299 2.976 -1.048 2.995 10.361
beta0_pH[8,1] 1.402 1.208 -1.588 1.526 3.597
beta0_pH[9,1] 2.455 1.745 -1.410 2.435 5.515
beta0_pH[10,1] 2.359 1.550 -1.174 2.473 5.050
beta0_pH[11,1] -0.939 0.627 -2.391 -0.875 0.174
beta0_pH[12,1] -0.884 0.699 -2.370 -0.864 0.564
beta0_pH[13,1] -0.914 0.644 -2.330 -0.867 0.320
beta0_pH[14,1] -0.933 0.702 -2.430 -0.888 0.425
beta0_pH[15,1] -0.975 0.651 -2.446 -0.909 0.178
beta0_pH[16,1] -1.114 0.661 -2.569 -1.038 0.064
beta0_pH[1,2] 2.551 0.370 1.795 2.574 3.150
beta0_pH[2,2] 2.774 0.322 1.939 2.826 3.266
beta0_pH[3,2] 2.525 0.395 1.744 2.531 3.277
beta0_pH[4,2] 2.621 0.323 1.827 2.673 3.124
beta0_pH[5,2] 3.287 1.162 1.612 3.147 5.838
beta0_pH[6,2] 2.818 0.411 1.899 2.838 3.523
beta0_pH[7,2] 1.964 0.318 1.369 1.982 2.481
beta0_pH[8,2] 2.612 0.629 0.719 2.754 3.264
beta0_pH[9,2] 2.909 0.620 1.631 3.002 3.882
beta0_pH[10,2] 3.071 0.778 1.314 3.209 4.153
beta0_pH[11,2] -2.730 0.028 -2.750 -2.740 -2.648
beta0_pH[12,2] -2.727 0.037 -2.750 -2.739 -2.625
beta0_pH[13,2] -2.725 0.041 -2.750 -2.739 -2.613
beta0_pH[14,2] -2.730 0.030 -2.750 -2.741 -2.648
beta0_pH[15,2] -2.724 0.049 -2.750 -2.740 -2.604
beta0_pH[16,2] -2.725 0.040 -2.750 -2.739 -2.617
beta0_pH[1,3] 1.121 0.383 0.223 1.169 1.629
beta0_pH[2,3] 1.561 0.589 0.064 1.652 2.421
beta0_pH[3,3] 2.009 0.460 0.811 2.032 2.758
beta0_pH[4,3] 1.983 0.867 -0.116 2.082 3.067
beta0_pH[5,3] 0.274 0.638 -1.410 0.336 1.387
beta0_pH[6,3] 0.069 0.696 -2.072 0.239 0.924
beta0_pH[7,3] 0.356 0.512 -1.323 0.468 0.919
beta0_pH[8,3] 0.296 0.213 -0.154 0.302 0.704
beta0_pH[9,3] 0.302 0.462 -0.844 0.360 1.036
beta0_pH[10,3] 0.292 0.387 -0.682 0.342 0.865
beta0_pH[11,3] -0.266 0.410 -1.252 -0.234 0.452
beta0_pH[12,3] -2.560 0.173 -2.745 -2.612 -2.113
beta0_pH[13,3] 0.065 0.372 -0.740 0.081 0.722
beta0_pH[14,3] -0.308 0.607 -1.217 -0.444 1.301
beta0_pH[15,3] -0.672 0.323 -1.327 -0.661 -0.089
beta0_pH[16,3] 0.051 0.309 -0.563 0.055 0.644
beta1_pH[1,1] 1.975 0.750 1.017 1.787 3.873
beta1_pH[2,1] 0.974 0.461 0.463 0.867 2.207
beta1_pH[3,1] 1.667 0.903 0.492 1.475 3.944
beta1_pH[4,1] 1.720 1.320 0.210 1.373 5.510
beta1_pH[5,1] 2.363 1.468 0.240 2.121 5.984
beta1_pH[6,1] 4.064 2.276 0.373 4.062 8.782
beta1_pH[7,1] 2.916 1.914 0.145 2.756 6.892
beta1_pH[8,1] 2.139 0.659 1.037 2.098 3.561
beta1_pH[9,1] 1.438 0.959 0.138 1.313 4.064
beta1_pH[10,1] 1.045 0.249 0.626 1.030 1.563
beta1_pH[11,1] 4.007 0.560 3.016 3.977 5.199
beta1_pH[12,1] 1.756 0.511 0.956 1.696 2.921
beta1_pH[13,1] 2.854 0.682 1.772 2.770 4.407
beta1_pH[14,1] 2.258 0.565 1.185 2.228 3.435
beta1_pH[15,1] 2.317 0.711 1.144 2.253 3.881
beta1_pH[16,1] 4.799 0.862 3.152 4.750 6.571
beta1_pH[1,2] 1.530 1.274 0.084 1.266 5.283
beta1_pH[2,2] 1.442 1.453 0.058 0.943 5.446
beta1_pH[3,2] 1.305 0.815 0.192 1.224 3.123
beta1_pH[4,2] 2.092 1.851 0.079 1.445 6.711
beta1_pH[5,2] 3.230 1.952 0.283 2.967 7.730
beta1_pH[6,2] 1.953 1.240 0.203 1.747 5.249
beta1_pH[7,2] 2.546 2.269 0.046 1.854 7.709
beta1_pH[8,2] 1.647 1.638 0.053 1.060 6.138
beta1_pH[9,2] 1.611 1.332 0.092 1.337 5.310
beta1_pH[10,2] 1.619 1.297 0.104 1.327 5.215
beta1_pH[11,2] 4.653 0.274 4.118 4.647 5.222
beta1_pH[12,2] 4.790 0.366 4.124 4.770 5.556
beta1_pH[13,2] 5.246 0.266 4.718 5.245 5.759
beta1_pH[14,2] 4.719 0.288 4.159 4.718 5.279
beta1_pH[15,2] 4.776 0.727 3.393 5.096 5.676
beta1_pH[16,2] 5.433 0.268 4.886 5.446 5.945
beta1_pH[1,3] 2.080 0.605 1.260 1.999 3.614
beta1_pH[2,3] 1.154 0.837 0.116 0.968 3.450
beta1_pH[3,3] 1.089 0.776 0.138 0.953 3.372
beta1_pH[4,3] 1.602 1.256 0.089 1.318 5.070
beta1_pH[5,3] 2.170 1.740 0.100 1.775 6.700
beta1_pH[6,3] 1.987 1.821 0.051 1.430 6.625
beta1_pH[7,3] 1.938 1.985 0.068 1.068 6.967
beta1_pH[8,3] 2.740 0.401 1.961 2.732 3.534
beta1_pH[9,3] 1.611 0.999 0.187 1.472 4.380
beta1_pH[10,3] 2.987 0.518 2.093 2.941 4.184
beta1_pH[11,3] 3.021 0.465 2.186 2.992 4.086
beta1_pH[12,3] 5.864 0.286 5.248 5.876 6.392
beta1_pH[13,3] 2.065 0.418 1.319 2.040 2.965
beta1_pH[14,3] 2.489 0.966 0.207 2.860 3.651
beta1_pH[15,3] 2.640 0.365 1.962 2.622 3.388
beta1_pH[16,3] 1.782 0.352 1.081 1.788 2.449
beta2_pH[1,1] 0.771 1.216 0.095 0.302 4.916
beta2_pH[2,1] 1.792 1.811 0.104 1.095 6.647
beta2_pH[3,1] 2.922 1.993 0.230 2.563 7.532
beta2_pH[4,1] 1.127 3.083 -5.387 1.223 7.003
beta2_pH[5,1] 2.588 2.420 -2.463 2.339 7.762
beta2_pH[6,1] 2.696 2.285 -1.141 2.412 7.760
beta2_pH[7,1] -0.406 3.690 -6.849 -0.558 6.930
beta2_pH[8,1] 2.279 1.982 0.172 1.685 7.229
beta2_pH[9,1] 2.152 2.783 -4.029 2.133 7.376
beta2_pH[10,1] 2.400 1.863 0.323 1.876 7.324
beta2_pH[11,1] 0.574 0.233 0.297 0.534 1.060
beta2_pH[12,1] 2.261 1.981 0.146 1.692 7.270
beta2_pH[13,1] 1.317 1.652 0.155 0.547 6.219
beta2_pH[14,1] 3.470 1.887 0.655 3.209 7.840
beta2_pH[15,1] 2.188 1.947 0.184 1.542 7.017
beta2_pH[16,1] 0.302 0.161 0.160 0.277 0.551
beta2_pH[1,2] 0.426 3.107 -6.379 0.899 6.103
beta2_pH[2,2] -1.815 2.736 -7.349 -1.662 3.797
beta2_pH[3,2] -2.617 2.302 -7.451 -2.409 1.962
beta2_pH[4,2] -2.585 2.547 -7.830 -2.442 2.670
beta2_pH[5,2] 0.627 2.967 -6.181 0.847 6.076
beta2_pH[6,2] -2.378 2.274 -7.642 -2.000 1.368
beta2_pH[7,2] -2.870 2.636 -8.225 -2.752 2.651
beta2_pH[8,2] -1.589 3.122 -7.460 -1.624 4.819
beta2_pH[9,2] -1.741 2.951 -7.536 -1.716 4.538
beta2_pH[10,2] -0.481 3.298 -7.318 -0.120 5.837
beta2_pH[11,2] -4.911 1.900 -9.168 -4.770 -1.785
beta2_pH[12,2] -2.011 1.371 -5.925 -1.592 -0.616
beta2_pH[13,2] -3.671 1.706 -7.841 -3.329 -1.306
beta2_pH[14,2] -3.294 1.706 -7.563 -2.845 -1.195
beta2_pH[15,2] -2.531 4.119 -8.764 -3.832 5.272
beta2_pH[16,2] -5.328 1.895 -9.563 -5.123 -2.235
beta2_pH[1,3] 2.658 1.807 0.229 2.329 6.854
beta2_pH[2,3] 1.497 2.393 -4.205 1.242 6.319
beta2_pH[3,3] -1.602 3.031 -7.054 -1.967 5.488
beta2_pH[4,3] 0.737 2.864 -5.746 0.911 5.996
beta2_pH[5,3] -0.186 3.086 -6.230 -0.218 5.955
beta2_pH[6,3] 0.255 3.195 -6.316 0.524 6.179
beta2_pH[7,3] -1.039 3.133 -6.549 -1.006 5.255
beta2_pH[8,3] 4.334 2.012 1.109 4.138 8.697
beta2_pH[9,3] 1.761 2.712 -4.738 1.758 6.749
beta2_pH[10,3] 1.658 1.453 0.368 1.106 5.798
beta2_pH[11,3] -1.426 0.934 -4.061 -1.132 -0.475
beta2_pH[12,3] -1.456 0.466 -2.525 -1.368 -0.904
beta2_pH[13,3] -2.415 1.625 -6.603 -1.925 -0.601
beta2_pH[14,3] -0.382 3.354 -6.175 -1.513 5.472
beta2_pH[15,3] -2.083 1.251 -5.449 -1.724 -0.721
beta2_pH[16,3] -2.497 1.543 -6.525 -2.081 -0.657
beta3_pH[1,1] 35.575 2.826 30.952 35.167 42.017
beta3_pH[2,1] 36.207 2.045 32.505 36.084 41.386
beta3_pH[3,1] 33.208 2.168 27.072 33.661 35.690
beta3_pH[4,1] 33.974 6.919 20.831 36.722 42.920
beta3_pH[5,1] 36.865 5.868 21.512 38.823 43.685
beta3_pH[6,1] 31.415 6.898 19.385 33.517 40.484
beta3_pH[7,1] 27.556 7.392 19.357 26.193 42.484
beta3_pH[8,1] 32.174 2.301 28.166 32.299 35.811
beta3_pH[9,1] 29.130 4.758 20.088 28.870 41.131
beta3_pH[10,1] 34.059 1.902 29.383 34.564 36.461
beta3_pH[11,1] 29.185 0.804 27.328 29.266 30.537
beta3_pH[12,1] 30.800 2.549 26.908 30.224 36.154
beta3_pH[13,1] 33.061 1.612 30.686 32.828 36.932
beta3_pH[14,1] 30.526 0.920 28.730 30.597 31.986
beta3_pH[15,1] 33.540 2.858 28.730 33.193 40.578
beta3_pH[16,1] 32.355 1.390 29.610 32.361 35.253
beta3_pH[1,2] 34.440 7.808 19.826 38.500 43.464
beta3_pH[2,2] 28.877 6.322 19.452 28.084 41.263
beta3_pH[3,2] 38.620 6.230 21.353 41.291 43.650
beta3_pH[4,2] 28.984 7.136 19.735 26.011 42.941
beta3_pH[5,2] 30.337 6.404 19.673 29.719 42.769
beta3_pH[6,2] 33.286 4.475 21.337 34.495 40.716
beta3_pH[7,2] 25.479 5.868 19.249 23.827 38.763
beta3_pH[8,2] 28.381 5.963 19.673 27.483 41.821
beta3_pH[9,2] 33.320 8.336 19.977 31.959 43.908
beta3_pH[10,2] 29.810 6.621 19.729 28.478 42.459
beta3_pH[11,2] 43.185 0.289 42.448 43.204 43.660
beta3_pH[12,2] 42.131 0.662 40.682 42.173 43.232
beta3_pH[13,2] 43.389 0.270 42.855 43.394 43.888
beta3_pH[14,2] 42.710 0.413 41.818 42.771 43.381
beta3_pH[15,2] 37.469 8.412 21.363 43.197 43.666
beta3_pH[16,2] 43.369 0.197 42.996 43.363 43.744
beta3_pH[1,3] 40.034 1.187 37.030 40.119 41.964
beta3_pH[2,3] 31.600 4.886 20.700 32.223 41.362
beta3_pH[3,3] 36.661 7.008 20.808 40.849 43.243
beta3_pH[4,3] 27.745 4.838 19.670 27.992 39.081
beta3_pH[5,3] 30.697 6.483 19.840 30.650 42.713
beta3_pH[6,3] 31.670 6.856 19.728 31.789 42.797
beta3_pH[7,3] 27.167 6.317 19.393 26.076 41.405
beta3_pH[8,3] 41.453 0.341 40.719 41.467 42.047
beta3_pH[9,3] 31.942 4.745 19.541 33.408 39.856
beta3_pH[10,3] 36.730 1.003 34.381 36.745 38.576
beta3_pH[11,3] 41.674 0.777 40.206 41.652 43.270
beta3_pH[12,3] 42.289 0.276 41.735 42.295 42.833
beta3_pH[13,3] 42.260 0.809 40.770 42.238 43.768
beta3_pH[14,3] 35.808 7.837 20.337 40.912 41.848
beta3_pH[15,3] 41.954 0.668 40.647 41.959 43.308
beta3_pH[16,3] 41.710 0.870 40.026 41.719 43.417
beta0_pelagic[1] 1.350 0.769 -0.338 1.545 2.374
beta0_pelagic[2] 0.981 0.572 -0.394 1.218 1.652
beta0_pelagic[3] 0.215 0.253 -0.417 0.251 0.604
beta0_pelagic[4] 0.211 0.378 -0.716 0.248 0.854
beta0_pelagic[5] 0.588 0.477 -0.606 0.678 1.228
beta0_pelagic[6] 0.572 0.445 -0.485 0.631 1.296
beta0_pelagic[7] 1.467 0.204 1.045 1.481 1.814
beta0_pelagic[8] 1.639 0.186 1.195 1.655 1.946
beta0_pelagic[9] 1.786 0.512 0.537 1.885 2.525
beta0_pelagic[10] 2.207 0.536 0.752 2.375 2.808
beta0_pelagic[11] -1.061 0.955 -3.488 -0.731 0.106
beta0_pelagic[12] 1.638 0.156 1.339 1.637 1.944
beta0_pelagic[13] 0.273 0.204 -0.191 0.292 0.621
beta0_pelagic[14] -0.247 0.276 -0.839 -0.227 0.230
beta0_pelagic[15] -0.315 0.147 -0.616 -0.313 -0.041
beta0_pelagic[16] -0.137 0.398 -1.293 -0.051 0.388
beta1_pelagic[1] 1.212 1.207 0.038 0.851 5.103
beta1_pelagic[2] 0.887 1.050 0.017 0.357 3.725
beta1_pelagic[3] 0.871 0.394 0.350 0.789 1.985
beta1_pelagic[4] 1.006 0.397 0.304 0.978 1.897
beta1_pelagic[5] 0.575 0.588 0.016 0.408 2.023
beta1_pelagic[6] 1.217 0.531 0.381 1.157 2.452
beta1_pelagic[7] 1.868 1.816 0.075 1.149 6.668
beta1_pelagic[8] 1.061 1.336 0.029 0.535 4.949
beta1_pelagic[9] 1.424 0.656 0.475 1.303 3.116
beta1_pelagic[10] 0.728 0.980 0.020 0.365 3.810
beta1_pelagic[11] 4.388 1.274 2.553 4.086 7.566
beta1_pelagic[12] 2.954 0.342 2.284 2.947 3.664
beta1_pelagic[13] 2.258 0.408 1.499 2.233 3.089
beta1_pelagic[14] 3.915 0.628 2.811 3.885 5.238
beta1_pelagic[15] 2.439 0.272 1.922 2.434 3.006
beta1_pelagic[16] 3.610 0.776 2.470 3.481 5.634
beta2_pelagic[1] 1.615 2.346 -3.766 1.393 6.518
beta2_pelagic[2] 0.735 2.386 -4.763 0.151 5.886
beta2_pelagic[3] 1.771 1.787 0.096 1.141 6.404
beta2_pelagic[4] 2.185 1.740 0.180 1.627 6.352
beta2_pelagic[5] 0.230 3.199 -5.940 0.156 6.470
beta2_pelagic[6] 2.167 1.907 0.132 1.693 6.705
beta2_pelagic[7] -1.926 1.841 -6.019 -1.641 1.417
beta2_pelagic[8] -2.088 2.595 -7.322 -1.958 3.503
beta2_pelagic[9] 1.836 1.893 0.080 1.100 6.688
beta2_pelagic[10] 1.226 2.069 -3.983 1.336 5.691
beta2_pelagic[11] 0.160 0.066 0.064 0.154 0.317
beta2_pelagic[12] 0.987 0.308 0.513 0.957 1.651
beta2_pelagic[13] 0.795 0.750 0.204 0.601 3.042
beta2_pelagic[14] 0.315 0.131 0.168 0.288 0.618
beta2_pelagic[15] 1.927 0.961 0.807 1.675 4.609
beta2_pelagic[16] 0.412 0.259 0.135 0.348 1.058
beta3_pelagic[1] 25.425 4.666 19.663 23.606 36.610
beta3_pelagic[2] 28.918 4.897 20.040 28.724 38.962
beta3_pelagic[3] 30.358 2.862 24.684 30.358 36.462
beta3_pelagic[4] 26.003 2.380 21.790 25.944 32.051
beta3_pelagic[5] 28.770 4.829 20.514 28.729 38.478
beta3_pelagic[6] 30.502 3.384 24.914 30.433 37.708
beta3_pelagic[7] 27.195 3.580 19.932 27.401 34.045
beta3_pelagic[8] 26.867 5.033 20.035 26.217 38.150
beta3_pelagic[9] 31.197 3.964 22.933 31.784 36.970
beta3_pelagic[10] 28.283 4.905 19.789 28.192 37.957
beta3_pelagic[11] 36.165 3.354 27.358 36.763 41.311
beta3_pelagic[12] 41.849 0.166 41.410 41.900 41.996
beta3_pelagic[13] 40.774 1.104 38.022 41.063 41.963
beta3_pelagic[14] 40.405 1.236 37.434 40.671 41.936
beta3_pelagic[15] 41.815 0.185 41.313 41.873 41.996
beta3_pelagic[16] 40.637 1.306 37.134 41.040 41.967
mu_beta0_pelagic[1] 0.630 0.749 -0.772 0.655 1.930
mu_beta0_pelagic[2] 1.346 0.512 0.309 1.382 2.249
mu_beta0_pelagic[3] -0.038 0.724 -1.577 0.044 1.061
tau_beta0_pelagic[1] 7.692 15.682 0.092 1.892 63.818
tau_beta0_pelagic[2] 2.507 3.921 0.190 1.593 10.462
tau_beta0_pelagic[3] 1.301 1.127 0.085 0.993 4.358
beta0_yellow[1] -0.457 0.212 -0.935 -0.440 -0.099
beta0_yellow[2] 0.356 0.230 -0.295 0.386 0.712
beta0_yellow[3] -0.408 0.285 -0.965 -0.373 -0.066
beta0_yellow[4] 0.312 0.540 -0.862 0.411 1.106
beta0_yellow[5] -1.771 0.494 -2.717 -1.781 -0.741
beta0_yellow[6] 0.078 0.394 -0.643 0.094 0.715
beta0_yellow[7] 0.193 1.425 -3.180 0.936 1.617
beta0_yellow[8] 0.749 0.543 -0.866 0.893 1.337
beta0_yellow[9] -0.188 0.445 -1.085 -0.153 0.573
beta0_yellow[10] 0.643 0.199 0.248 0.640 1.036
beta0_yellow[11] -3.584 0.277 -4.114 -3.582 -3.021
beta0_yellow[12] -2.902 0.905 -4.163 -3.187 -1.286
beta0_yellow[13] -4.108 0.340 -4.798 -4.083 -3.520
beta0_yellow[14] -3.490 0.328 -4.097 -3.505 -2.837
beta0_yellow[15] -3.820 0.293 -4.464 -3.795 -3.277
beta0_yellow[16] -3.628 0.306 -4.231 -3.629 -3.038
beta1_yellow[1] 0.450 0.512 0.013 0.297 1.912
beta1_yellow[2] 1.326 0.504 0.712 1.214 2.854
beta1_yellow[3] 0.789 0.552 0.276 0.704 2.193
beta1_yellow[4] 2.581 1.180 0.914 2.397 5.142
beta1_yellow[5] 4.298 1.740 1.478 4.095 8.249
beta1_yellow[6] 3.322 1.672 0.822 2.988 7.270
beta1_yellow[7] 2.987 1.970 0.122 2.864 7.276
beta1_yellow[8] 1.933 1.619 0.118 1.454 6.154
beta1_yellow[9] 1.998 0.783 0.465 1.981 3.562
beta1_yellow[10] 2.432 0.545 1.460 2.410 3.610
beta1_yellow[11] 3.536 0.374 2.848 3.534 4.274
beta1_yellow[12] 3.460 1.913 0.228 3.699 6.568
beta1_yellow[13] 3.338 0.466 2.525 3.285 4.329
beta1_yellow[14] 3.532 0.554 2.639 3.498 4.573
beta1_yellow[15] 2.870 0.390 2.153 2.847 3.738
beta1_yellow[16] 3.341 0.397 2.603 3.331 4.134
beta2_yellow[1] -0.752 2.945 -6.151 -0.873 5.748
beta2_yellow[2] -1.645 1.522 -5.825 -1.251 -0.099
beta2_yellow[3] -2.328 1.814 -6.586 -1.988 -0.065
beta2_yellow[4] -0.493 1.034 -4.040 -0.151 -0.052
beta2_yellow[5] -3.217 1.775 -7.298 -2.962 -0.648
beta2_yellow[6] 2.986 1.943 0.112 2.747 7.193
beta2_yellow[7] 0.218 3.394 -5.904 -0.248 5.429
beta2_yellow[8] -1.516 2.849 -7.096 -1.460 4.814
beta2_yellow[9] 2.549 2.057 -1.822 2.336 6.978
beta2_yellow[10] -3.226 1.843 -7.494 -2.967 -0.567
beta2_yellow[11] -0.769 0.291 -1.446 -0.726 -0.389
beta2_yellow[12] -0.127 1.756 -4.291 -0.068 4.540
beta2_yellow[13] -0.650 0.264 -1.326 -0.594 -0.307
beta2_yellow[14] -0.720 0.307 -1.401 -0.683 -0.262
beta2_yellow[15] -0.772 0.396 -1.657 -0.689 -0.357
beta2_yellow[16] -0.804 0.260 -1.419 -0.768 -0.428
beta3_yellow[1] 28.509 4.804 20.106 28.533 38.189
beta3_yellow[2] 29.314 1.868 24.876 29.217 33.192
beta3_yellow[3] 31.751 2.292 26.176 31.846 35.999
beta3_yellow[4] 29.613 3.602 22.619 29.463 36.489
beta3_yellow[5] 32.462 1.126 30.246 32.528 34.163
beta3_yellow[6] 38.209 2.516 29.270 38.614 40.761
beta3_yellow[7] 27.007 2.918 22.036 26.878 34.275
beta3_yellow[8] 28.671 3.743 21.642 28.663 36.054
beta3_yellow[9] 35.877 2.916 25.827 36.536 39.104
beta3_yellow[10] 29.184 0.743 27.453 29.291 30.317
beta3_yellow[11] 41.871 0.153 41.479 41.919 41.998
beta3_yellow[12] 33.373 3.209 29.100 32.984 40.709
beta3_yellow[13] 41.699 0.362 40.659 41.822 41.994
beta3_yellow[14] 41.750 0.783 40.985 41.897 41.997
beta3_yellow[15] 41.779 0.249 41.105 41.861 41.995
beta3_yellow[16] 41.886 0.129 41.542 41.928 41.998
mu_beta0_yellow[1] -0.059 0.413 -0.919 -0.062 0.739
mu_beta0_yellow[2] -0.076 0.615 -1.460 -0.026 1.009
mu_beta0_yellow[3] -3.458 0.547 -4.197 -3.568 -2.022
tau_beta0_yellow[1] 5.622 9.150 0.237 2.969 29.594
tau_beta0_yellow[2] 0.936 0.881 0.085 0.706 3.335
tau_beta0_yellow[3] 14.574 28.095 0.127 3.834 96.310
beta0_black[1] -0.078 0.161 -0.385 -0.080 0.244
beta0_black[2] 1.683 0.307 0.762 1.742 2.075
beta0_black[3] 1.198 0.248 0.568 1.238 1.548
beta0_black[4] 1.816 0.397 0.600 1.904 2.293
beta0_black[5] 1.389 1.107 -0.769 1.354 3.396
beta0_black[6] 1.391 1.074 -0.553 1.353 3.594
beta0_black[7] 1.360 1.039 -0.848 1.353 3.338
beta0_black[8] 1.157 0.313 0.436 1.197 1.634
beta0_black[9] 1.663 0.515 0.697 1.645 2.592
beta0_black[10] 1.358 0.148 1.067 1.359 1.633
beta0_black[11] 3.336 0.276 2.765 3.370 3.724
beta0_black[12] 4.412 0.201 4.015 4.414 4.809
beta0_black[13] -0.079 0.240 -0.563 -0.075 0.365
beta0_black[14] 1.830 0.633 0.237 1.994 2.670
beta0_black[15] 1.028 0.388 0.072 1.103 1.521
beta0_black[16] 3.212 1.044 0.516 3.541 4.360
beta2_black[1] 3.119 1.693 0.781 2.816 7.020
beta2_black[2] -1.931 2.213 -6.620 -1.666 2.582
beta2_black[3] -0.152 3.034 -6.036 -0.076 5.919
beta2_black[4] -1.990 1.860 -6.389 -1.441 -0.062
beta2_black[5] -0.024 3.083 -6.283 -0.022 5.996
beta2_black[6] -0.010 3.150 -6.228 0.032 6.221
beta2_black[7] 0.091 3.114 -5.972 0.081 6.301
beta2_black[8] -3.012 2.180 -7.416 -2.890 1.416
beta2_black[9] -1.366 2.449 -6.642 -0.984 3.992
beta2_black[10] -1.083 2.766 -6.262 -1.209 5.434
beta2_black[11] -1.645 2.341 -6.419 -1.480 3.988
beta2_black[12] -2.921 1.636 -6.941 -2.579 -0.729
beta2_black[13] -2.016 1.414 -5.727 -1.623 -0.415
beta2_black[14] -0.985 1.451 -5.626 -0.341 -0.077
beta2_black[15] -1.785 2.061 -6.624 -1.314 1.823
beta2_black[16] 1.824 2.144 -2.634 1.430 6.579
beta3_black[1] 41.801 0.856 40.144 41.902 43.066
beta3_black[2] 30.029 7.811 19.239 30.620 44.209
beta3_black[3] 27.782 7.419 19.186 26.973 44.484
beta3_black[4] 32.938 3.711 22.446 32.889 40.587
beta3_black[5] 31.679 7.359 19.674 31.328 44.900
beta3_black[6] 31.895 7.374 19.767 31.515 45.041
beta3_black[7] 32.153 7.274 19.879 31.877 44.982
beta3_black[8] 28.382 7.786 20.221 23.283 42.883
beta3_black[9] 34.308 8.383 19.623 35.141 45.253
beta3_black[10] 28.362 8.939 19.337 24.641 45.355
beta3_black[11] 33.745 4.422 29.110 32.282 44.812
beta3_black[12] 32.882 0.746 31.472 32.943 33.846
beta3_black[13] 39.254 0.823 37.446 39.335 40.632
beta3_black[14] 37.792 3.851 29.813 38.185 45.161
beta3_black[15] 35.959 5.107 29.140 35.230 45.407
beta3_black[16] 33.624 4.175 29.132 32.240 43.718
beta4_black[1] -0.283 0.200 -0.679 -0.283 0.092
beta4_black[2] 0.280 0.188 -0.099 0.280 0.657
beta4_black[3] -1.008 0.192 -1.400 -1.006 -0.636
beta4_black[4] 0.657 0.232 0.210 0.660 1.117
beta4_black[5] -0.059 3.091 -6.281 -0.018 5.987
beta4_black[6] -0.063 3.178 -6.382 -0.066 6.234
beta4_black[7] -0.010 3.239 -6.074 -0.015 6.350
beta4_black[8] -0.858 0.386 -1.624 -0.858 -0.108
beta4_black[9] 2.133 1.112 0.278 2.012 4.683
beta4_black[10] 0.033 0.188 -0.334 0.037 0.396
beta4_black[11] -0.714 0.232 -1.170 -0.716 -0.250
beta4_black[12] 0.598 0.355 -0.069 0.594 1.311
beta4_black[13] -1.286 0.225 -1.718 -1.289 -0.833
beta4_black[14] -0.043 0.250 -0.510 -0.046 0.448
beta4_black[15] -0.950 0.232 -1.429 -0.945 -0.481
beta4_black[16] -0.569 0.249 -1.073 -0.566 -0.082
mu_beta0_black[1] 1.050 0.927 -0.816 1.116 2.501
mu_beta0_black[2] 1.337 0.645 0.106 1.361 2.333
mu_beta0_black[3] 2.005 1.139 -0.709 2.129 3.743
tau_beta0_black[1] 1.308 1.474 0.052 0.982 4.396
tau_beta0_black[2] 23.578 48.070 0.120 6.676 148.582
tau_beta0_black[3] 0.318 0.221 0.028 0.273 0.891
sigma_H[1] 0.227 0.048 0.141 0.223 0.330
sigma_H[2] 0.175 0.029 0.124 0.172 0.241
sigma_H[3] 0.185 0.041 0.113 0.181 0.274
sigma_H[4] 0.333 0.085 0.188 0.326 0.525
sigma_H[5] 1.015 0.212 0.625 1.004 1.450
sigma_H[6] 0.373 0.186 0.042 0.365 0.778
sigma_H[7] 0.291 0.058 0.200 0.285 0.420
sigma_H[8] 0.350 0.147 0.116 0.352 0.589
sigma_H[9] 0.520 0.123 0.324 0.505 0.798
sigma_H[10] 0.208 0.042 0.134 0.205 0.295
sigma_H[11] 0.271 0.046 0.192 0.267 0.367
sigma_H[12] 0.431 0.166 0.205 0.403 0.767
sigma_H[13] 0.217 0.037 0.153 0.214 0.297
sigma_H[14] 0.499 0.092 0.338 0.491 0.699
sigma_H[15] 0.248 0.039 0.181 0.244 0.332
sigma_H[16] 0.219 0.042 0.153 0.214 0.315
lambda_H[1] 3.556 5.173 0.170 2.008 15.668
lambda_H[2] 9.282 8.411 0.942 6.918 31.176
lambda_H[3] 6.793 9.710 0.322 3.477 32.004
lambda_H[4] 0.007 0.004 0.001 0.006 0.018
lambda_H[5] 1.812 4.054 0.018 0.432 13.041
lambda_H[6] 4.965 12.790 0.006 0.097 39.133
lambda_H[7] 0.016 0.011 0.003 0.014 0.046
lambda_H[8] 6.987 9.715 0.117 3.538 33.327
lambda_H[9] 0.016 0.011 0.003 0.013 0.045
lambda_H[10] 0.427 1.090 0.040 0.255 1.683
lambda_H[11] 0.259 0.395 0.011 0.122 1.260
lambda_H[12] 4.754 6.222 0.212 2.777 21.813
lambda_H[13] 3.656 3.253 0.256 2.706 11.977
lambda_H[14] 3.405 3.971 0.247 2.175 14.280
lambda_H[15] 0.024 0.028 0.003 0.016 0.091
lambda_H[16] 0.977 1.375 0.056 0.555 4.509
mu_lambda_H[1] 4.399 1.851 1.352 4.232 8.321
mu_lambda_H[2] 3.448 1.907 0.427 3.263 7.350
mu_lambda_H[3] 3.502 1.844 0.791 3.154 7.692
sigma_lambda_H[1] 8.789 4.213 2.165 8.212 18.273
sigma_lambda_H[2] 7.534 4.678 0.621 6.897 17.987
sigma_lambda_H[3] 6.242 3.945 1.048 5.316 16.373
beta_H[1,1] 6.980 1.042 4.527 7.146 8.574
beta_H[2,1] 9.895 0.454 8.927 9.909 10.743
beta_H[3,1] 8.005 0.713 6.341 8.078 9.158
beta_H[4,1] 10.665 7.692 -4.561 10.608 26.111
beta_H[5,1] -0.131 2.851 -5.938 0.025 4.956
beta_H[6,1] 2.117 4.617 -8.044 3.215 8.475
beta_H[7,1] 1.988 5.251 -9.682 2.411 11.212
beta_H[8,1] 1.090 2.937 -2.747 1.084 3.505
beta_H[9,1] 13.578 5.652 2.886 13.427 25.516
beta_H[10,1] 7.221 1.512 4.148 7.245 10.174
beta_H[11,1] 4.848 3.606 -3.124 5.572 9.888
beta_H[12,1] 2.568 1.047 0.681 2.516 4.894
beta_H[13,1] 9.054 0.948 7.155 9.141 10.500
beta_H[14,1] 2.201 1.009 0.181 2.210 4.230
beta_H[15,1] -6.232 3.718 -12.970 -6.434 1.481
beta_H[16,1] 3.149 2.404 -0.827 2.894 8.807
beta_H[1,2] 7.925 0.249 7.428 7.932 8.382
beta_H[2,2] 10.037 0.130 9.785 10.040 10.293
beta_H[3,2] 8.970 0.191 8.603 8.967 9.354
beta_H[4,2] 3.186 1.480 0.217 3.190 6.085
beta_H[5,2] 1.927 1.007 -0.027 1.925 3.800
beta_H[6,2] 5.417 1.246 2.721 5.582 7.385
beta_H[7,2] 2.163 1.038 0.358 2.091 4.405
beta_H[8,2] 3.058 0.897 1.465 3.132 4.388
beta_H[9,2] 3.252 1.097 1.074 3.225 5.459
beta_H[10,2] 8.199 0.318 7.525 8.214 8.781
beta_H[11,2] 9.806 0.653 8.823 9.686 11.200
beta_H[12,2] 3.961 0.360 3.298 3.949 4.730
beta_H[13,2] 9.136 0.254 8.687 9.124 9.658
beta_H[14,2] 4.041 0.358 3.363 4.041 4.753
beta_H[15,2] 11.387 0.679 10.002 11.415 12.662
beta_H[16,2] 4.532 0.807 3.029 4.521 6.169
beta_H[1,3] 8.506 0.236 8.102 8.487 9.017
beta_H[2,3] 10.113 0.110 9.900 10.108 10.337
beta_H[3,3] 9.677 0.154 9.382 9.680 9.979
beta_H[4,3] -1.823 0.981 -3.701 -1.859 0.205
beta_H[5,3] 4.104 0.692 2.634 4.124 5.394
beta_H[6,3] 8.626 1.338 6.538 8.621 11.083
beta_H[7,3] -2.193 0.728 -3.696 -2.153 -0.834
beta_H[8,3] 5.378 0.472 4.687 5.310 6.373
beta_H[9,3] -2.496 0.740 -4.057 -2.460 -1.107
beta_H[10,3] 8.711 0.262 8.199 8.699 9.243
beta_H[11,3] 8.548 0.289 7.934 8.571 9.063
beta_H[12,3] 5.296 0.307 4.600 5.329 5.817
beta_H[13,3] 8.852 0.177 8.492 8.859 9.178
beta_H[14,3] 5.772 0.278 5.172 5.790 6.273
beta_H[15,3] 10.377 0.322 9.753 10.376 11.022
beta_H[16,3] 6.445 0.527 5.325 6.475 7.380
beta_H[1,4] 8.333 0.182 7.934 8.344 8.655
beta_H[2,4] 10.191 0.107 9.973 10.197 10.394
beta_H[3,4] 10.165 0.158 9.820 10.178 10.441
beta_H[4,4] 11.913 0.476 10.998 11.903 12.821
beta_H[5,4] 6.035 0.896 4.579 5.928 7.994
beta_H[6,4] 6.829 1.008 4.745 7.002 8.337
beta_H[7,4] 8.100 0.336 7.432 8.096 8.754
beta_H[8,4] 6.867 0.319 6.343 6.830 7.543
beta_H[9,4] 7.173 0.460 6.276 7.164 8.092
beta_H[10,4] 7.889 0.239 7.440 7.888 8.394
beta_H[11,4] 9.406 0.198 9.016 9.405 9.800
beta_H[12,4] 7.158 0.209 6.756 7.161 7.594
beta_H[13,4] 9.073 0.141 8.788 9.074 9.358
beta_H[14,4] 7.770 0.220 7.332 7.771 8.210
beta_H[15,4] 9.483 0.233 9.037 9.478 9.934
beta_H[16,4] 9.316 0.218 8.924 9.302 9.787
beta_H[1,5] 9.003 0.152 8.693 9.011 9.289
beta_H[2,5] 10.792 0.087 10.626 10.789 10.974
beta_H[3,5] 10.916 0.167 10.611 10.908 11.265
beta_H[4,5] 8.456 0.397 7.671 8.465 9.227
beta_H[5,5] 5.294 0.724 3.557 5.396 6.430
beta_H[6,5] 8.968 0.680 7.919 8.836 10.443
beta_H[7,5] 6.881 0.326 6.240 6.879 7.519
beta_H[8,5] 8.244 0.194 7.883 8.239 8.623
beta_H[9,5] 8.211 0.469 7.276 8.217 9.155
beta_H[10,5] 9.987 0.225 9.526 9.990 10.429
beta_H[11,5] 11.495 0.221 11.055 11.496 11.929
beta_H[12,5] 8.489 0.185 8.132 8.488 8.848
beta_H[13,5] 10.018 0.127 9.781 10.013 10.279
beta_H[14,5] 9.200 0.224 8.789 9.191 9.683
beta_H[15,5] 11.175 0.244 10.706 11.171 11.659
beta_H[16,5] 9.925 0.166 9.586 9.926 10.230
beta_H[1,6] 10.171 0.190 9.835 10.157 10.590
beta_H[2,6] 11.507 0.107 11.293 11.506 11.720
beta_H[3,6] 10.820 0.153 10.498 10.829 11.095
beta_H[4,6] 12.780 0.704 11.428 12.762 14.146
beta_H[5,6] 5.958 0.704 4.682 5.914 7.464
beta_H[6,6] 8.535 0.803 6.512 8.723 9.650
beta_H[7,6] 9.724 0.542 8.649 9.721 10.788
beta_H[8,6] 9.485 0.252 9.010 9.488 9.923
beta_H[9,6] 8.448 0.773 6.940 8.442 10.049
beta_H[10,6] 9.583 0.292 8.959 9.605 10.096
beta_H[11,6] 10.827 0.347 10.111 10.850 11.438
beta_H[12,6] 9.375 0.247 8.906 9.364 9.902
beta_H[13,6] 11.046 0.161 10.762 11.037 11.379
beta_H[14,6] 9.823 0.284 9.240 9.830 10.375
beta_H[15,6] 10.820 0.426 9.964 10.827 11.655
beta_H[16,6] 10.539 0.230 10.021 10.555 10.962
beta_H[1,7] 10.911 0.816 8.969 11.004 12.247
beta_H[2,7] 12.190 0.411 11.371 12.191 12.997
beta_H[3,7] 10.588 0.595 9.316 10.633 11.602
beta_H[4,7] 2.739 3.549 -4.078 2.762 9.752
beta_H[5,7] 6.638 2.417 2.440 6.423 12.233
beta_H[6,7] 9.793 3.009 4.570 9.496 17.491
beta_H[7,7] 11.066 2.729 5.659 11.035 16.336
beta_H[8,7] 10.893 0.916 9.375 10.829 12.673
beta_H[9,7] 4.494 4.037 -3.748 4.582 12.158
beta_H[10,7] 9.713 1.324 7.136 9.663 12.522
beta_H[11,7] 11.011 1.709 7.790 10.884 14.520
beta_H[12,7] 9.983 0.908 8.016 10.059 11.534
beta_H[13,7] 11.639 0.755 9.913 11.729 12.766
beta_H[14,7] 10.398 0.919 8.435 10.443 12.010
beta_H[15,7] 12.154 2.232 7.864 12.149 16.577
beta_H[16,7] 12.237 1.193 10.261 12.093 15.051
beta0_H[1] 8.888 12.662 -16.272 8.704 34.570
beta0_H[2] 10.775 5.725 -0.934 10.846 22.755
beta0_H[3] 9.956 9.102 -8.487 9.770 29.870
beta0_H[4] 11.120 177.167 -343.142 11.731 361.275
beta0_H[5] 3.748 34.718 -66.687 4.271 73.406
beta0_H[6] 8.886 66.507 -133.284 7.910 155.697
beta0_H[7] 8.294 119.997 -223.986 4.827 251.097
beta0_H[8] 5.700 19.184 -21.621 6.534 29.335
beta0_H[9] 7.878 117.474 -228.072 7.488 244.889
beta0_H[10] 8.746 29.673 -50.501 8.500 70.605
beta0_H[11] 10.330 51.155 -96.884 10.659 118.782
beta0_H[12] 7.038 10.936 -14.270 7.052 29.461
beta0_H[13] 10.060 11.767 -11.048 9.974 29.486
beta0_H[14] 6.706 11.040 -16.225 7.023 28.515
beta0_H[15] 7.104 110.250 -212.511 8.234 234.079
beta0_H[16] 8.038 21.585 -38.607 7.933 53.567